class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testLeftTailGrad(self, dtype, do_compile):
      x = np.linspace(-50.0, -8.0, 1000).astype(dtype)
  
      @tf.function(autograph=False, jit_compile=do_compile)
      def fn(x):
          return tf.math.log(softplus.Softplus().forward(x))
  
      _, grad = gradient.value_and_gradient(fn, x)
  
      true_grad = 1 / (1 + np.exp(-x)) / np.log1p(np.exp(x))
      self.assertAllClose(true_grad, self.evaluate(grad), atol=1e-3)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 20 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testLeftTailGrad(dtype, do_compile) and returns: tf